Journal of the American Statistical Association

最新文章

• Minimax Optimal Procedures for Locally Private Estimation Rejoinder AI翻译
  作者：Duchi, John C.; Jordan, Michael I.; Wainwright, Martin J.
  作者单位：Stanford University; University of California System; University of California Berkeley; University of California System; University of California Berkeley
• Robust High-Dimensional Volatility Matrix Estimation for High-Frequency Factor Model AI翻译
  作者：Fan, Jianqing; Kim, Donggyu
  作者单位：Princeton University
  摘要：High-frequency financial data allow us to estimate large volatility matrices with relatively short time horizon. Many novel statistical methods have been introduced to address large volatility matrix estimation problems from a high-dimensional Ito process with microstructural noise contamination. Their asymptotic theories require sub-Gaussian or some finite high-order moments assumptions for observed log-returns. These assumptions are at odd with the heavy tail phenomenon that is pandemic in f...
• Analysis of Gap Times Based on Panel Count Data With Informative Observation Times and Unknown Start Time AI翻译
  作者：Ma, Ling; Sundaram, Rajeshwari
  作者单位：National Institutes of Health (NIH) - USA; NIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
  摘要：In biomedical studies, one is often interested in repeat events with longitudinal observations occurring only intermittently, resulting in panel count data. The first stage of labor, measured through unit-increments of cervical dilation in pregnant women, provides such an example. Obstetricians are interested in assessing the gap time distribution of per-unit increments of cervical dilation for better management of labor process. Typically, only intermittent medical examinations for cervical d...
• An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations AI翻译
  作者：Mak, Simon; Sung, Chih-Li; Wang, Xingjian; Yeh, Shiang-Ting; Chang, Yu-Hung; Joseph, V. Roshan; Yang, Vigor; Wu, C. F. Jeff
  作者单位：University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
  摘要：In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations, and statistical modeling. In this article, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engi...
• Particle EM for Variable Selection AI翻译
  作者：Rockova, Veronika
  作者单位：University of Chicago
  摘要：Despite its long history of success, the EM algorithm has been vulnerable to local entrapment when the posterior/likelihood is multi-modal. This is particularly pronounced in spike-and-slab posterior distributions for Bayesian variable selection. The main thrust of this article is to introduce the particle EM algorithm, a new population-based optimization strategy that harvests multiple modes in search spaces that present many local maxima. Motivated by nonparametric variational Bayes strategi...
• Identifying Latent Structures in Restricted Latent Class Models AI翻译
  作者：Xu, Gongjun; Shang, Zhuoran
  作者单位：University of Michigan System; University of Michigan; University of Minnesota System; University of Minnesota Twin Cities
  摘要：This article focuses on a family of restricted latent structure models with wide applications in psychological and educational assessment, where the model parameters are restricted via a latent structure matrix to reflect prespecified assumptions on the latent attributes. Such a latent matrix is often provided by experts and assumed to be correct upon construction, yet it may be subjective and misspecified. Recognizing this problem, researchers have been developing methods to estimate the matr...
• Fast Moment Estimation for Generalized Latent Dirichlet Models AI翻译
  作者：Zhao, Shiwen; Engelhardt, Barbara E.; Mukherjee, Sayan; Dunson, David B.
  作者单位：Duke University; Princeton University; Princeton University
  摘要：We develop a generalized method of moments (GMM) approach for fast parameter estimation in a new class of Dirichlet latent variable models with mixed data types. Parameter estimation via GMM has computational and statistical advantages over alternative methods, such as expectation maximization, variational inference, and Markov chain Monte Carlo. A key computational advantage of our method, Moment Estimation for latent Dirichlet models (MELD), is that parameter estimation does not require inst...
• On the Use of Reproducing Kernel Hilbert Spaces in Functional Classification AI翻译
  作者：Berrendero, Jose R.; Cuevas, Antonio; Torrecilla, Jose L.
  作者单位：Autonomous University of Madrid; Universidad Carlos III de Madrid
  摘要：The Hajek-Feldman dichotomy establishes that two Gaussian measures are either mutually absolutely continuous with respect to each other (and hence there is a Radon-Nikodym density for each measure with respect to the other one) or mutually singular. Unlike the case of finite-dimensional Gaussian measures, there are nontrivial examples of both situations when dealing with Gaussian stochastic processes. This article provides: (a) Explicit expressions for the optimal (Bayes) rule and the minimal ...
• Bayesian Approximate Kernel Regression With Variable Selection AI翻译
  作者：Crawford, Lorin; Wood, Kris C.; Zhou, Xiang; Mukherjee, Sayan
  作者单位：Brown University; Brown University; Brown University; Duke University; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; Duke University; Duke University; Duke University; Duke University
  摘要：Nonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selection for kernel regression models is a challenge partly because, unlike the linear regression setting, there is no clear concept of an effect size for regression coefficients. In this article, we propose a novel framework that provides an effect size analog for each explanatory variable in Bayesian kernel regression models when the kernel is shif...
• A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data AI翻译
  作者：Zhou, Haiming; Hanson, Timothy
  作者单位：Northern Illinois University; University of South Carolina System; University of South Carolina Columbia; Medtronic
  摘要：A comprehensive, unified approach to modeling arbitrarily censored spatial survival data is presented for the three most commonly used semiparametric models: proportional hazards, proportional odds, and accelerated failure time. Unlike many other approaches, all manner of censored survival times are simultaneously accommodated including uncensored, interval censored, current-status, left and right censored, and mixtures of these. Left-truncated data are also accommodated leading to models for ...